// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 Benoit Jacob <jacob.benoit.1@gmail.com>
// Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

#define TEST_ENABLE_TEMPORARY_TRACKING
#define EIGEN_NO_STATIC_ASSERT

#include "main.h"

template<typename ArrayType>
void
vectorwiseop_array(const ArrayType& m)
{
	typedef typename ArrayType::Scalar Scalar;
	typedef Array<Scalar, ArrayType::RowsAtCompileTime, 1> ColVectorType;
	typedef Array<Scalar, 1, ArrayType::ColsAtCompileTime> RowVectorType;

	Index rows = m.rows();
	Index cols = m.cols();
	Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);

	ArrayType m1 = ArrayType::Random(rows, cols), m2(rows, cols), m3(rows, cols);

	ColVectorType colvec = ColVectorType::Random(rows);
	RowVectorType rowvec = RowVectorType::Random(cols);

	// test addition

	m2 = m1;
	m2.colwise() += colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);

	VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
	VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());

	m2 = m1;
	m2.rowwise() += rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);

	VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
	VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());

	// test substraction

	m2 = m1;
	m2.colwise() -= colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);

	VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
	VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());

	m2 = m1;
	m2.rowwise() -= rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);

	VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
	VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());

	// test multiplication

	m2 = m1;
	m2.colwise() *= colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() * colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) * colvec);

	VERIFY_RAISES_ASSERT(m2.colwise() *= colvec.transpose());
	VERIFY_RAISES_ASSERT(m1.colwise() * colvec.transpose());

	m2 = m1;
	m2.rowwise() *= rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() * rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) * rowvec);

	VERIFY_RAISES_ASSERT(m2.rowwise() *= rowvec.transpose());
	VERIFY_RAISES_ASSERT(m1.rowwise() * rowvec.transpose());

	// test quotient

	m2 = m1;
	m2.colwise() /= colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() / colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) / colvec);

	VERIFY_RAISES_ASSERT(m2.colwise() /= colvec.transpose());
	VERIFY_RAISES_ASSERT(m1.colwise() / colvec.transpose());

	m2 = m1;
	m2.rowwise() /= rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() / rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) / rowvec);

	VERIFY_RAISES_ASSERT(m2.rowwise() /= rowvec.transpose());
	VERIFY_RAISES_ASSERT(m1.rowwise() / rowvec.transpose());

	m2 = m1;
	// yes, there might be an aliasing issue there but ".rowwise() /="
	// is supposed to evaluate " m2.colwise().sum()" into a temporary to avoid
	// evaluating the reduction multiple times
	if (ArrayType::RowsAtCompileTime > 2 || ArrayType::RowsAtCompileTime == Dynamic) {
		m2.rowwise() /= m2.colwise().sum();
		VERIFY_IS_APPROX(m2, m1.rowwise() / m1.colwise().sum());
	}

	// all/any
	Array<bool, Dynamic, Dynamic> mb(rows, cols);
	mb = (m1.real() <= 0.7).colwise().all();
	VERIFY((mb.col(c) == (m1.real().col(c) <= 0.7).all()).all());
	mb = (m1.real() <= 0.7).rowwise().all();
	VERIFY((mb.row(r) == (m1.real().row(r) <= 0.7).all()).all());

	mb = (m1.real() >= 0.7).colwise().any();
	VERIFY((mb.col(c) == (m1.real().col(c) >= 0.7).any()).all());
	mb = (m1.real() >= 0.7).rowwise().any();
	VERIFY((mb.row(r) == (m1.real().row(r) >= 0.7).any()).all());
}

template<typename MatrixType>
void
vectorwiseop_matrix(const MatrixType& m)
{
	typedef typename MatrixType::Scalar Scalar;
	typedef typename NumTraits<Scalar>::Real RealScalar;
	typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> ColVectorType;
	typedef Matrix<Scalar, 1, MatrixType::ColsAtCompileTime> RowVectorType;
	typedef Matrix<RealScalar, MatrixType::RowsAtCompileTime, 1> RealColVectorType;
	typedef Matrix<RealScalar, 1, MatrixType::ColsAtCompileTime> RealRowVectorType;
	typedef Matrix<Scalar, Dynamic, Dynamic> MatrixX;

	Index rows = m.rows();
	Index cols = m.cols();
	Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);

	MatrixType m1 = MatrixType::Random(rows, cols), m2(rows, cols), m3(rows, cols);

	ColVectorType colvec = ColVectorType::Random(rows);
	RowVectorType rowvec = RowVectorType::Random(cols);
	RealColVectorType rcres;
	RealRowVectorType rrres;

	// test broadcast assignment
	m2 = m1;
	m2.colwise() = colvec;
	for (Index j = 0; j < cols; ++j)
		VERIFY_IS_APPROX(m2.col(j), colvec);
	m2.rowwise() = rowvec;
	for (Index i = 0; i < rows; ++i)
		VERIFY_IS_APPROX(m2.row(i), rowvec);
	if (rows > 1)
		VERIFY_RAISES_ASSERT(m2.colwise() = colvec.transpose());
	if (cols > 1)
		VERIFY_RAISES_ASSERT(m2.rowwise() = rowvec.transpose());

	// test addition

	m2 = m1;
	m2.colwise() += colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() + colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) + colvec);

	if (rows > 1) {
		VERIFY_RAISES_ASSERT(m2.colwise() += colvec.transpose());
		VERIFY_RAISES_ASSERT(m1.colwise() + colvec.transpose());
	}

	m2 = m1;
	m2.rowwise() += rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() + rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) + rowvec);

	if (cols > 1) {
		VERIFY_RAISES_ASSERT(m2.rowwise() += rowvec.transpose());
		VERIFY_RAISES_ASSERT(m1.rowwise() + rowvec.transpose());
	}

	// test substraction

	m2 = m1;
	m2.colwise() -= colvec;
	VERIFY_IS_APPROX(m2, m1.colwise() - colvec);
	VERIFY_IS_APPROX(m2.col(c), m1.col(c) - colvec);

	if (rows > 1) {
		VERIFY_RAISES_ASSERT(m2.colwise() -= colvec.transpose());
		VERIFY_RAISES_ASSERT(m1.colwise() - colvec.transpose());
	}

	m2 = m1;
	m2.rowwise() -= rowvec;
	VERIFY_IS_APPROX(m2, m1.rowwise() - rowvec);
	VERIFY_IS_APPROX(m2.row(r), m1.row(r) - rowvec);

	if (cols > 1) {
		VERIFY_RAISES_ASSERT(m2.rowwise() -= rowvec.transpose());
		VERIFY_RAISES_ASSERT(m1.rowwise() - rowvec.transpose());
	}

	// ------ partial reductions ------

#define TEST_PARTIAL_REDUX_BASIC(FUNC, ROW, COL, PREPROCESS)                                                           \
	{                                                                                                                  \
		ROW = m1 PREPROCESS.colwise().FUNC;                                                                            \
		for (Index k = 0; k < cols; ++k)                                                                               \
			VERIFY_IS_APPROX(ROW(k), m1.col(k) PREPROCESS.FUNC);                                                       \
		COL = m1 PREPROCESS.rowwise().FUNC;                                                                            \
		for (Index k = 0; k < rows; ++k)                                                                               \
			VERIFY_IS_APPROX(COL(k), m1.row(k) PREPROCESS.FUNC);                                                       \
	}

	TEST_PARTIAL_REDUX_BASIC(sum(), rowvec, colvec, EIGEN_EMPTY);
	TEST_PARTIAL_REDUX_BASIC(prod(), rowvec, colvec, EIGEN_EMPTY);
	TEST_PARTIAL_REDUX_BASIC(mean(), rowvec, colvec, EIGEN_EMPTY);
	TEST_PARTIAL_REDUX_BASIC(minCoeff(), rrres, rcres, .real());
	TEST_PARTIAL_REDUX_BASIC(maxCoeff(), rrres, rcres, .real());
	TEST_PARTIAL_REDUX_BASIC(norm(), rrres, rcres, EIGEN_EMPTY);
	TEST_PARTIAL_REDUX_BASIC(squaredNorm(), rrres, rcres, EIGEN_EMPTY);
	TEST_PARTIAL_REDUX_BASIC(redux(internal::scalar_sum_op<Scalar, Scalar>()), rowvec, colvec, EIGEN_EMPTY);

	VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum(), m1.colwise().template lpNorm<1>());
	VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().sum(), m1.rowwise().template lpNorm<1>());
	VERIFY_IS_APPROX(m1.cwiseAbs().colwise().maxCoeff(), m1.colwise().template lpNorm<Infinity>());
	VERIFY_IS_APPROX(m1.cwiseAbs().rowwise().maxCoeff(), m1.rowwise().template lpNorm<Infinity>());

	// regression for bug 1158
	VERIFY_IS_APPROX(m1.cwiseAbs().colwise().sum().x(), m1.col(0).cwiseAbs().sum());

	// test normalized
	m2 = m1.colwise().normalized();
	VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
	m2 = m1.rowwise().normalized();
	VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());

	// test normalize
	m2 = m1;
	m2.colwise().normalize();
	VERIFY_IS_APPROX(m2.col(c), m1.col(c).normalized());
	m2 = m1;
	m2.rowwise().normalize();
	VERIFY_IS_APPROX(m2.row(r), m1.row(r).normalized());

	// test with partial reduction of products
	Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m1m1 = m1 * m1.transpose();
	VERIFY_IS_APPROX((m1 * m1.transpose()).colwise().sum(), m1m1.colwise().sum());
	Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> tmp(rows);
	VERIFY_EVALUATION_COUNT(tmp = (m1 * m1.transpose()).colwise().sum(), 1);

	m2 = m1.rowwise() - (m1.colwise().sum() / RealScalar(m1.rows())).eval();
	m1 = m1.rowwise() - (m1.colwise().sum() / RealScalar(m1.rows()));
	VERIFY_IS_APPROX(m1, m2);
	VERIFY_EVALUATION_COUNT(m2 = (m1.rowwise() - m1.colwise().sum() / RealScalar(m1.rows())),
							(MatrixType::RowsAtCompileTime != 1 ? 1 : 0));

	// test empty expressions
	VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
	VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().sum().eval(), MatrixX::Zero(1, cols));
	VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().sum().eval(), MatrixX::Zero(rows, 1));
	VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().sum().eval(), MatrixX::Zero(1, cols));

	VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
	VERIFY_IS_APPROX(m1.matrix().middleRows(0, 0).colwise().prod().eval(), MatrixX::Ones(1, cols));
	VERIFY_IS_APPROX(m1.matrix().middleCols(0, fix<0>).rowwise().prod().eval(), MatrixX::Ones(rows, 1));
	VERIFY_IS_APPROX(m1.matrix().middleRows(0, fix<0>).colwise().prod().eval(), MatrixX::Ones(1, cols));

	VERIFY_IS_APPROX(m1.matrix().middleCols(0, 0).rowwise().squaredNorm().eval(), MatrixX::Zero(rows, 1));

	VERIFY_RAISES_ASSERT(m1.real().middleCols(0, 0).rowwise().minCoeff().eval());
	VERIFY_RAISES_ASSERT(m1.real().middleRows(0, 0).colwise().maxCoeff().eval());
	VERIFY_IS_EQUAL(m1.real().middleRows(0, 0).rowwise().maxCoeff().eval().rows(), 0);
	VERIFY_IS_EQUAL(m1.real().middleCols(0, 0).colwise().maxCoeff().eval().cols(), 0);
	VERIFY_IS_EQUAL(m1.real().middleRows(0, fix<0>).rowwise().maxCoeff().eval().rows(), 0);
	VERIFY_IS_EQUAL(m1.real().middleCols(0, fix<0>).colwise().maxCoeff().eval().cols(), 0);
}

EIGEN_DECLARE_TEST(vectorwiseop)
{
	CALL_SUBTEST_1(vectorwiseop_array(Array22cd()));
	CALL_SUBTEST_2(vectorwiseop_array(Array<double, 3, 2>()));
	CALL_SUBTEST_3(vectorwiseop_array(ArrayXXf(3, 4)));
	CALL_SUBTEST_4(vectorwiseop_matrix(Matrix4cf()));
	CALL_SUBTEST_5(vectorwiseop_matrix(Matrix4f()));
	CALL_SUBTEST_5(vectorwiseop_matrix(Vector4f()));
	CALL_SUBTEST_5(vectorwiseop_matrix(Matrix<float, 4, 5>()));
	CALL_SUBTEST_6(vectorwiseop_matrix(
		MatrixXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE), internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
	CALL_SUBTEST_7(vectorwiseop_matrix(VectorXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
	CALL_SUBTEST_7(vectorwiseop_matrix(RowVectorXd(internal::random<int>(1, EIGEN_TEST_MAX_SIZE))));
}
